# Detection Method Based on Automatic Visual Shape Clustering for Pin-Missing Defect in Transmission Lines

arXiv: https://arxiv.org/abs/2001.06236 · HTML: https://mlsanity.com/paper/2001.06236/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Deep Learning in Automated Power Line Inspection: A Review](https://mlsanity.com/paper/2502.07826/) (2025)
- [TaylorMade VDD: Domain-adaptive Visual Defect Detector for High-mix Low-volume Production of Non-convex Cylindrical Metal Objects](https://mlsanity.com/paper/2104.04203/) (2021)
- [Taguchi based Design of Sequential Convolution Neural Network for Classification of Defective Fasteners](https://mlsanity.com/paper/2207.10992/) (2022)
- [A Segmentation-driven Editing Method for Bolt Defect Augmentation and Detection](https://mlsanity.com/paper/2508.10509/) (2025)

## Abstract (arXiv)

Bolts are the most numerous fasteners in transmission lines and are prone to losing their split pins. How to realize the automatic pin-missing defect detection for bolts in transmission lines so as to achieve timely and efficient trouble shooting is a difficult problem and the long-term research target of power systems. In this paper, an automatic detection model called Automatic Visual Shape Clustering Network (AVSCNet) for pin-missing defect is constructed. Firstly, an unsupervised clustering method for the visual shapes of bolts is proposed and applied to construct a defect detection model which can learn the difference of visual shape. Next, three deep convolutional neural network optimization methods are used in the model: the feature enhancement, feature fusion and region feature extraction. The defect detection results are obtained by applying the regression calculation and classification to the regional features. In this paper, the object detection model of different networks is used to test the dataset of pin-missing defect constructed by the aerial images of transmission lines from multiple locations, and it is evaluated by various indicators and is fully verified. The results show that our method can achieve considerably satisfactory detection effect.